The Scenario
You run talent research for a growing SaaS company. Every quarter, someone hands you a list of job boards and asks for a market mapping report — what roles are competitors hiring for, at what pay bands, in which cities. The last person in this seat kept a running Excel file and updated it by reading job postings one at a time.
The file has 50 job posting URLs from three different boards in column A. The boards are all JavaScript-rendered — standard HTTP requests return nothing useful.
The bad version:
- Open each URL in Chrome, wait for the page to render, read the job title, copy it, switch to the sheet, paste it.
- Realize "salary" isn't listed on twelve of the fifty postings. Decide what to put in those cells. Go back and check them again two hours later when someone asks why they're blank.
- Lose the thread on which company was which after the browser has thirty tabs open.
This is work that looks like research but functions as transcription. And transcription at fifty rows with JavaScript rendering delays is not a task that rewards patience — it punishes it.
The Easy Way: One Prompt in SheetXAI
SheetXAI is an AI agent that lives inside your Google Sheet. It reads the sheet, understands your URL list, and through its built-in ScrapingBee integration it fires headless browser requests — JavaScript rendering included — and writes structured fields back into your columns.
For each job posting URL in column A, use ScrapingBee with JavaScript rendering enabled to extract job title, company name, location, and salary range and write them into columns B, C, D, and E.
What You Get
- Column B: job title as listed on the posting.
- Column C: company name extracted from the page header or meta fields.
- Column D: location — city, state, or "Remote" as it appears on the page.
- Column E: salary range when listed; blank with a note in column F if the field is absent.
- All 50 rows processed with JavaScript rendering — no manual browser tabs.
What If the Data Is Not Quite Ready
Salary formats vary wildly across postings — hourly rates, annual ranges, per-month figures, all mixed together
For each URL in column A, use ScrapingBee with JS rendering to extract the salary field into column E, then normalize it in column F — convert hourly rates to annual equivalents (multiply by 2080), express all values as USD annual ranges, and flag ambiguous entries with "NORMALIZE MANUALLY."
Some job boards paginate — the URL in column A is a listing page, not a single job posting
For each URL in column A that resolves to a job listing page (not a single posting), use ScrapingBee with JavaScript rendering to extract all job titles and company names visible on that page — write one row per listing into a new sheet called Expanded Listings, carrying over the source URL from column A.
You need to join the scraped data against a skills requirements list in a second tab
After scraping job title, company, and location into columns B, C, and D using ScrapingBee, check whether each job title in column B contains any of the role keywords listed in column A of the Skills Requirements tab — write "MATCH" into column E if it does, "NO MATCH" if it doesn't.
Kill chain: scrape all 50 postings, normalize salary, join against target role list, and output only the rows that match your hiring targets
For each URL in column A, use ScrapingBee with JS rendering to extract job title, company, location, and salary — normalize salary to USD annual, compare job titles against target roles listed in the Hiring Targets sheet, and output only matching rows into a new sheet called Qualified Leads with all five columns and a Salary Normalized column added.
One prompt replaces what used to be an afternoon of tabs, copy-paste, and second-guessing what "Competitive Compensation" means in dollars.
Try It
Get the 7-day free trial of SheetXAI and open any Google Sheet with a column of job board URLs, then ask it to extract job data using ScrapingBee with JavaScript rendering. See also: Bulk Scrape Competitor Prices Into a Google Sheet and the full ScrapingBee integration overview.